Sequence_data_cluster_analysis
Sylvie nolf
2024-05-23
library(readxl, quietly = T)
library(dplyr, quietly = T)
library(data.table, quietly = T)
library(phyloseq, quietly = T)
library(tidyverse, quietly = T)
library(stringr, quietly = T)
library(DescTools, quietly = T)
library(ggplot2, quietly = T)
library(tidyr, quietly = T)
library(vegan, quietly = T)
library(phyloseq.extended, quietly = T)
library(rstatix, quietly = T)
library(ggpubr, quietly = T)
library(rmarkdown)Parsing BLAST Output
The output from the blastn search described earlier needs to be wrangled a bit to get it into a usable format. The below code takes the raw data from looking like this:
read_tsv("Z:/PRJ-endophyte/UNITE_public_sym_consensus_sequences_backup_resume.BlastResult", col_names = F, show_col_types = F,n_max = 10)|>paged_table()to looking like this:
# read in blast data and subset to only clusters with more than one read, and take the top hit for each cluster.
blast_data_sym <- read_tsv("Z:/PRJ-endophyte/UNITE_public_sym_consensus_sequences_backup_resume.BlastResult", col_names = F, show_col_types = F)
#first for symptomatic tree data
columns =c("qseqid","sseqid","pident","length","mismatch","gapopen","qstart","qend","sstart","send","evalue","bitscore")
colnames(blast_data_sym)<-columns
blast_data_sym$centroid_size=gsub(".*;seqs=","",blast_data_sym$qseqid)
blast_data_sym$centroid_id=gsub("centroid=","",blast_data_sym$qseqid)
blast_data_sym$centroid_id=gsub(";seqs=.*","",blast_data_sym$centroid_id)
blast_data_sym<-blast_data_sym %>% relocate(centroid_id,centroid_size)
blast_data_sym<-subset(blast_data_sym,blast_data_sym$centroid_size>1)
blast_data_sym2 <- blast_data_sym[!duplicated(blast_data_sym$centroid_id), ]
blast_data_sym2$SH=str_split_i(blast_data_sym2$sseqid,"\\|",3)
#add in separate column for each taxonomic rank
matrix_temp=str_split_fixed(blast_data_sym2$sseqid,"\\|",3)
tax_df=data.frame("UDB"=matrix_temp[,1],"taxa"=matrix_temp[,2],"SH"=matrix_temp[,3])
tax_df$UDB<-gsub(">","",tax_df$UDB)
matrix_temp_2=str_split_fixed(tax_df$taxa,";",7)
tax_df$kingdom=gsub(".__","",matrix_temp_2[,1])
tax_df$phylum=gsub(".__","",matrix_temp_2[,2])
tax_df$class=gsub(".__","",matrix_temp_2[,3])
tax_df$order=gsub(".__","",matrix_temp_2[,4])
tax_df$family=gsub(".__","",matrix_temp_2[,5])
tax_df$genus=gsub(".__","",matrix_temp_2[,6])
tax_df$species=gsub(".__","",matrix_temp_2[,7])
merged_df_sym=cbind(blast_data_sym2,tax_df)[, -15]
#next for asymptomatic tree data
blast_data_asym_1 <- read_tsv("Z:/PRJ-endophyte/UNITE_public_asym_consensus_sequences_backup.BlastResult", col_names = F, show_col_types = F)
blast_data_asym_2 <- read_tsv("Z:/PRJ-endophyte/UNITE_public_resume_con_backup_resume.BlastResult", col_names = F, show_col_types = F)
blast_data_asym<-rbind(blast_data_asym_1,blast_data_asym_2)
colnames(blast_data_asym)<-columns
blast_data_asym$centroid_size=gsub(".*;seqs=","",blast_data_asym$qseqid)
blast_data_asym$centroid_id=gsub("centroid=","",blast_data_asym$qseqid)
blast_data_asym$centroid_id=gsub(";seqs=.*","",blast_data_asym$centroid_id)
blast_data_asym<-blast_data_asym %>% relocate(centroid_id,centroid_size)
blast_data_asym<-subset(blast_data_asym,blast_data_asym$centroid_size>1)
blast_data_asym2 <- blast_data_asym[!duplicated(blast_data_asym$centroid_id), ]
blast_data_asym2$SH=str_split_i(blast_data_asym2$sseqid,"\\|",3)
#add in separate column for each taxonomic rank
matrix_asymtemp=str_split_fixed(blast_data_asym2$sseqid,"\\|",3)
tax_df_asym=data.frame("UDB"=matrix_asymtemp[,1],"taxa"=matrix_asymtemp[,2],"SH"=matrix_asymtemp[,3])
tax_df_asym$UDB<-gsub(">","",tax_df_asym$UDB)
matrix_asymtemp=str_split_fixed(tax_df_asym$taxa,";",7)
tax_df_asym$kingdom=gsub(".__","",matrix_asymtemp[,1])
tax_df_asym$phylum=gsub(".__","",matrix_asymtemp[,2])
tax_df_asym$class=gsub(".__","",matrix_asymtemp[,3])
tax_df_asym$order=gsub(".__","",matrix_asymtemp[,4])
tax_df_asym$family=gsub(".__","",matrix_asymtemp[,5])
tax_df_asym$genus=gsub(".__","",matrix_asymtemp[,6])
tax_df_asym$species=gsub(".__","",matrix_asymtemp[,7])
merged_df_asym=cbind(blast_data_asym2,tax_df_asym)[, -15]merged_df_sym|>paged_table()merged_df_asym|>paged_table()Parsing VSearch clustering output
Similarly, vsearch outputs a strange single table that combines three separate tables, and need to be separated. The below code parses this and makes three neat data frames from each table. The one we will be using is the hit table referred to in the code as h df, and ends up looking like this:
#read in vsearch cluster output table to contstruct OTU table for input to phyloseq
#first for asymptomatic samples
asym_df<-read_tsv("Z:/PRJ-endophyte/ucout_asym_reads.out", col_names = F, show_col_types = F)
c_asym_df<-subset(asym_df,X1=="C")[,c("X2","X3","X9")]
colnames(c_asym_df)=c("cluster_number","cluster_size","centroid_label")
s_asym_df<-subset(asym_df,X1=="S")[,c("X2","X3","X9")]
colnames(s_asym_df)=c("cluster_number","centroid_length","centroid_label")
h_asym_df<-subset(asym_df,X1=="H")[,-c(1,6,7)]
colnames(h_asym_df)=c("cluster_number","query_length","percent_similiarity_to_centroid","match_orientation","alignment","query_label","centroid_label")
#then for symptomatic
sym_df<-read_tsv("Z:/PRJ-endophyte/ucout_sym_reads.out", col_names = F, show_col_types = F)
c_sym_df<-subset(sym_df,X1=="C")[,c("X2","X3","X9")]
colnames(c_sym_df)=c("cluster_number","cluster_size","centroid_label")
s_sym_df<-subset(sym_df,X1=="S")[,c("X2","X3","X9")]
colnames(s_sym_df)=c("cluster_number","centroid_length","centroid_label")
h_sym_df<-subset(sym_df,X1=="H")[,-c(1,6,7)]
colnames(h_sym_df)=c("cluster_number","query_length","percent_similiarity_to_centroid","match_orientation","alignment","query_label","centroid_label")h_sym_df|>paged_table()h_asym_df|>paged_table()Creating phyloseq object
The phyloseq package which will be used for further analysis requires 3 tables as inputs. 1: An OTU table which contains information on how many reads in each cluster were in each sample, 2: a taxonomy table showing the identification of each cluster at each taxonomic level, and 3: a sample table containing the names of each sample and any additional information to be used in analysis. The following code creates each of these in the format necesary and creates a phyloseq object
OTU table for phyloseq
#read in summary of sequencing run to match read_ids to barcodes
sequence_run_df<-read_tsv("Z:/PRJ-endophyte/sylvie/sequencing_raw/SN_Lib_1/sequencing_summary_FAX75472_80510af4_f3fa7a0a.txt")## Rows: 463331 Columns: 43
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (22): filename_fastq, filename_fast5, filename_pod5, parent_read_id, rea...
## dbl (20): channel, mux, minknow_events, start_time, duration, template_start...
## lgl (1): passes_filtering
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.h_asym_df<-merge(h_asym_df,sequence_run_df[,c(5,25)],by.x = "query_label",by.y="read_id",all.x = TRUE,all.y=FALSE)
h_sym_df<-merge(h_sym_df,sequence_run_df[,c(5,25)],by.x = "query_label",by.y="read_id",all.x = TRUE,all.y=FALSE)
#create otu table
asym_otu_table<-table(h_asym_df$centroid_label,h_asym_df$alias)
sym_otu_table<-table(h_sym_df$centroid_label,h_sym_df$alias)
merged_h_df<-rbind(h_sym_df,h_asym_df)
merged_otu_table<-table(merged_h_df$centroid_label,merged_h_df$alias)
merged_otu_table|>as.data.frame.matrix()|>paged_table()Taxonomy table for phyloseq
#create taxonomy data frame for input into phyloseq
asym_tax_df_for_phyloseq<-merged_df_asym[,c("centroid_id","kingdom","phylum","class","order","family","genus","species")]
sym_tax_df_for_phyloseq<-merged_df_sym[,c("centroid_id","kingdom","phylum","class","order","family","genus","species")]
merged_tax_df_for_phyloseq<-rbind(sym_tax_df_for_phyloseq,asym_tax_df_for_phyloseq)
sample_df_asym=data.frame("barcode"=unique(h_asym_df$alias),"sample_type"="Asymptomatic tree")|>drop_na()
sample_df_sym=data.frame("barcode"=unique(h_sym_df$alias),"sample_type"="Symptomatic healthy leaf")|>drop_na()
merged_sample_df_proto=rbind(sample_df_asym,sample_df_sym)
merged_tax_df_for_phyloseq <- merged_tax_df_for_phyloseq %>% tibble::column_to_rownames("centroid_id")
merged_tax_df_for_phyloseq|>paged_table()Sample table for phyloseq
# create dataframe with sample information for input to phyloseq
barcode_index=data.frame("barcode"=c(paste("barcode0",4:9,sep=""), paste("barcode",10:23,sep="")),"sample_id"=c(paste("Asymptomatic tree ",1:10,sep=""),paste("Symptomatic tree healthy leaves ",1:10,sep="")))
colnames(merged_otu_table)=barcode_index$sample_id
merged_sample_df<-merge(merged_sample_df_proto,barcode_index,by.x="barcode",by.y="barcode")
merged_sample_df|>paged_table()Create phyloseq object
#convert types of sample, otu, and taxonomy tables to be the right format for input into phyloseq package
merged_sample_df <- merged_sample_df %>%
tibble::column_to_rownames("sample_id")
otu_matrix=as.matrix(as.data.frame.matrix(merged_otu_table))
tax_matrix=as.matrix(merged_tax_df_for_phyloseq)
#create phyloseq object
OTU = otu_table(otu_matrix,taxa_are_rows = TRUE)
TAX = tax_table(tax_matrix)
samples = sample_data(merged_sample_df)
phyloseq_object=phyloseq(OTU,TAX, samples)
phyloseq_object## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1857 taxa and 20 samples ]
## sample_data() Sample Data: [ 20 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 1857 taxa by 7 taxonomic ranks ]#also create another object without all the fungi that are unidentified at the phylum level
phylo_object_no_incertae<-subset_taxa(phyloseq_object, !(phylum %in% c("Fungi_phy_Incertae_sedis")))
#normalise abundances to be out of 100%
phylo_object_no_incertae_transformed = transform_sample_counts(phylo_object_no_incertae,
function(x) 100 * x/sum(x))Analysis
Rarefaction curve
The rarefaction curve can tell you if its likely that all diversity was captured in your study. Luckily, phyloseq extended has a convenient inbuilt function for this.
#plot rarefaction curve
p<-ggrare(phyloseq_object, step = 1000, color = "sample_type", se = FALSE)+scale_color_discrete(labels=c("Asymptomatic field trees","Symptomatic field trees\n(healthy leaves)"), name="Group")+theme_classic()## rarefying sample Asymptomatic tree 1
## rarefying sample Asymptomatic tree 2
## rarefying sample Asymptomatic tree 3
## rarefying sample Asymptomatic tree 4
## rarefying sample Asymptomatic tree 5
## rarefying sample Asymptomatic tree 6
## rarefying sample Asymptomatic tree 7
## rarefying sample Asymptomatic tree 8
## rarefying sample Asymptomatic tree 9
## rarefying sample Asymptomatic tree 10
## rarefying sample Symptomatic tree healthy leaves 1
## rarefying sample Symptomatic tree healthy leaves 2
## rarefying sample Symptomatic tree healthy leaves 3
## rarefying sample Symptomatic tree healthy leaves 4
## rarefying sample Symptomatic tree healthy leaves 5
## rarefying sample Symptomatic tree healthy leaves 6
## rarefying sample Symptomatic tree healthy leaves 7
## rarefying sample Symptomatic tree healthy leaves 8
## rarefying sample Symptomatic tree healthy leaves 9
## rarefying sample Symptomatic tree healthy leaves 10
p
Shannon diversity
Phyloseq also has convenient functions for calculating the Shannon diversity indices of each sample, which is done below. The Wilcoxon rank-sum test is performed to compare the indices between asymptomatic and symptomatic trees.
#calculate shannon diversity and perform wilcox rank sum test to compare mean sizes of two groups
richness_estimates<-estimate_richness(phyloseq_object, measures="Shannon")
richness_estimates$sample_type<-str_split_i(rownames(richness_estimates)," ",1)
#wilcox rank-sum test
stat.test <- wilcox_test(data=richness_estimates,Shannon~sample_type) %>%
add_significance()
stat.test## # A tibble: 1 × 8
## .y. group1 group2 n1 n2 statistic p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 Shannon Asymptomatic Symptomatic 10 10 22 0.0355 *This can be nicely visualised with a box plot.
#plot shannon diversity
ggboxplot(data=richness_estimates,x="sample_type",y="Shannon", add = "jitter")+stat_compare_means(method="wilcox.test",label.x = 1.3, label.y = 5.5)+xlab("")+scale_x_discrete(labels=c("Asymptomatic field trees","Symptomatic field trees\n(healthy leaves)"))+ylab("Shannon diversity index")
Comparing abundance of taxa
Below a number of barcharts are made using phyloseq and ggplot to compare abundances of different fungal taxonomic ranks and samples.
More information on each of these figures can be found in the captions in the thesis.
#create a series of charts comparing abundance of different taxa
plot_A<-plot_bar(phyloseq_object, x = "Sample", y = "Abundance", fill ="phylum") +
geom_bar(aes(color=phylum, fill=phylum), stat="identity", position="stack")+
theme_classic()+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))+
theme(legend.position = "none")
plot_B<-plot_bar(phyloseq_object, x = "sample_type", y = "Abundance", fill ="phylum") +
geom_bar(aes(color=phylum, fill=phylum), stat="identity", position="stack")+
theme_classic()+
xlab("Sample type")+
scale_x_discrete(labels=c("Aysmptomatic tree","Symptomatic tree healthy leaves"))
plot_C<-plot_bar(phylo_object_no_incertae, x = "Sample", y = "Abundance", fill ="phylum") + geom_bar(aes(color=phylum, fill=phylum), stat="identity", position="stack")+
theme_classic()+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))+
theme(legend.position = "none")
plot_D<-plot_bar(phylo_object_no_incertae, x = "sample_type", y = "Abundance", fill ="phylum") + geom_bar(aes(color=phylum, fill=phylum), stat="identity", position="stack")+
xlab("Sample type")+
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust = 0.5))+
theme_classic()+
scale_x_discrete(labels=c("Aysmptomatic tree","Symptomatic tree healthy leaves"))
ggarrange(plot_A, plot_B, plot_C,plot_D,
labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)
require("fantaxtic")## Loading required package: fantaxtic##
## Attaching package: 'fantaxtic'## The following object is masked from 'package:phyloseq.extended':
##
## top_taxatop_nested <- nested_top_taxa(phylo_object_no_incertae_transformed,
top_tax_level = "phylum",
nested_tax_level = "family",
n_top_taxa = 3,
n_nested_taxa = 10)
# Plot the relative abundances at two levels.
plot_nested_bar(ps_obj = top_nested$ps_obj,
top_level = "phylum",
nested_level = "family",
sample_order = c(paste("Asymptomatic tree ",1:10,sep=""),paste("Symptomatic tree healthy leaves ",1:10,sep="")))+
guides(fill=guide_legend(nrow=10))+
theme(legend.key=element_blank(), legend.key.size=unit(1,"line"),legend.position = "top")+
ylab("Relative Abundance")+
scale_x_discrete(labels=c(paste("Asymptomatic tree ",1:10,sep=""),paste("Symptomatic tree ",1:10,"\n(healthy leaves)",sep="")))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `phylum = fct_relevel(.f = phylum, label, after = pos)`.
## Caused by warning:
## ! 1 unknown level in `f`: Other
Analysis of cluster data
How many clusters were identified to each taxonomic rank?
#calculate how many clusters were identified to each level
merged_h_df_with_sample_groups<-merge(merged_h_df,barcode_index,by.x="alias",by.y="barcode",all.x=TRUE,all.y=FALSE)
merged_h_df_with_sample_groups$sample_type<-str_split_i(merged_h_df_with_sample_groups$sample_id," ",1)
tax_df_with_sample_types<-merge(data.frame(phyloseq_object@tax_table),merged_h_df_with_sample_groups[,c("centroid_label","sample_type")],by.x=0, by.y="centroid_label",all.x=TRUE,all.y=FALSE)
with_phylum=tax_df_with_sample_types[!grepl(".*phy_Incertae_sedis",tax_df_with_sample_types$phylum),]
with_class=tax_df_with_sample_types[!grepl(".*cls_Incertae_sedis",tax_df_with_sample_types$class),]
with_order=tax_df_with_sample_types[!grepl(".*ord_Incertae_sedis",tax_df_with_sample_types$order),]
with_family=tax_df_with_sample_types[!grepl(".*fam_Incertae_sedis",tax_df_with_sample_types$family),]
with_genus=tax_df_with_sample_types[!grepl(".*fam_Incertae_sedis",tax_df_with_sample_types$family),]
with_species=tax_df_with_sample_types[!grepl(".*_sp",tax_df_with_sample_types$species),]
print("number of cluster identified as fungi")## [1] "number of cluster identified as fungi"length(unique(tax_df_with_sample_types$Row.names))## [1] 1857print("number of clusters with phylum:")## [1] "number of clusters with phylum:"length(unique(with_phylum$Row.names))## [1] 475print("number of clusters with class:")## [1] "number of clusters with class:"length(unique(c(with_class$Row.names)))## [1] 469print("number of clusters with order")## [1] "number of clusters with order"length(unique(c(with_order$Row.names)))## [1] 464print("number of clusters with family")## [1] "number of clusters with family"length(unique(c(with_family$Row.names)))## [1] 437print("number of clusters with genus")## [1] "number of clusters with genus"length(unique(c(with_genus$Row.names)))## [1] 437print("number of clusters with species")## [1] "number of clusters with species"length(unique(c(with_species$Row.names)))## [1] 353How big were the clusters?
#plot sizes of clusters
ggplot() +
geom_jitter(aes(x="Symptomatic trees\n(healthy leaves)", y=(c_sym_df$cluster_size[c_sym_df$cluster_size>1])), width=0.2, alpha=.2)+
geom_jitter(aes(x="Asymptomatic trees", y=(c_asym_df$cluster_size[c_asym_df$cluster_size>1])),width = 0.2, alpha=.2)+
geom_boxplot(aes(x="Symptomatic trees\n(healthy leaves)", y=(c_sym_df$cluster_size[c_sym_df$cluster_size>1])),outlier.shape = NA, alpha=0) +
geom_boxplot(aes(x= "Asymptomatic trees", y=(c_asym_df$cluster_size[c_asym_df$cluster_size>1])),outlier.shape = NA,alpha=0) +
scale_y_log10()+
theme_classic()+
xlab("")+
ylab("Size of clusters with more than one read")
Which taxa were present in each sample group?
#create csv listing the number of reads in clusters in each species
temp_asym=data.frame("centoid_id"=merged_df_asym$centroid_id,"centroid_size"=as.numeric(merged_df_asym$centroid_size),"phylum"=merged_df_asym$phylum,"family"=merged_df_asym$family,"species"=merged_df_asym$species,"group"="asymptomatic")
temp_sym=data.frame("centoid_id"=merged_df_sym$centroid_id,"centroid_size"=as.numeric(merged_df_sym$centroid_size),"phylum"=merged_df_sym$phylum,"family"=merged_df_sym$family,"species"=merged_df_sym$species,"group"="symptomatic")
summary_df<-rbind(temp_asym,temp_sym)
grouped_asym<-temp_asym|>group_by(species,group,phylum,family)|>summarise(num_reads=sum(centroid_size))## `summarise()` has grouped output by 'species', 'group', 'phylum'. You can
## override using the `.groups` argument.grouped_sym<-temp_sym|>group_by(species,group,phylum,family)|>summarise(num_reads=sum(centroid_size))## `summarise()` has grouped output by 'species', 'group', 'phylum'. You can
## override using the `.groups` argument.fun_df<-merge(grouped_asym,grouped_sym,by="species",all=TRUE)
fun_df <- fun_df %>% mutate(phylum.y = ifelse(is.na(phylum.y), phylum.x, phylum.y))
fun_df <- fun_df %>% mutate(family.y = ifelse(is.na(family.y), family.x, family.y))
final_df_family_counts<-subset(fun_df, select=c("phylum.y","family.y","species","num_reads.x","num_reads.y"))
colnames(final_df_family_counts)=c("phylum","family","species","asym_num_reads","sym_num_reads")
final_df_family_counts<-final_df_family_counts[order(final_df_family_counts$phylum,final_df_family_counts$family, final_df_family_counts$species),]
final_df_family_counts$species=gsub("_", " ",final_df_family_counts$species)
final_df_family_counts$family=gsub("_", " ",final_df_family_counts$family)
final_df_family_counts$phylum=gsub("_", " ",final_df_family_counts$phylum)
final_df_family_counts|>paged_table()#write.csv(final_df_family_counts,"species_counts_by_group.csv")Information on versions and session
sessionInfo()## R version 4.3.3 (2024-02-29 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
## [3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.utf8
##
## time zone: Australia/Sydney
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] fantaxtic_0.2.0 rmarkdown_2.26
## [3] ggpubr_0.6.0 rstatix_0.7.2
## [5] phyloseq.extended_0.1.4.1 vegan_2.6-4
## [7] lattice_0.22-6 permute_0.9-7
## [9] DescTools_0.99.54 lubridate_1.9.3
## [11] forcats_1.0.0 stringr_1.5.1
## [13] purrr_1.0.2 readr_2.1.5
## [15] tidyr_1.3.1 tibble_3.2.1
## [17] ggplot2_3.5.1 tidyverse_2.0.0
## [19] phyloseq_1.46.0 data.table_1.15.2
## [21] dplyr_1.1.4 readxl_1.4.3
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3
## [4] farver_2.1.1 fs_1.6.4 zlibbioc_1.48.2
## [7] vctrs_0.6.5 multtest_2.58.0 memoise_2.0.1
## [10] RCurl_1.98-1.14 ggtree_3.8.2 htmltools_0.5.8.1
## [13] broom_1.0.5 cellranger_1.1.0 Rhdf5lib_1.24.2
## [16] rhdf5_2.46.1 gridGraphics_0.5-1 sass_0.4.9
## [19] bslib_0.7.0 plyr_1.8.9 rootSolve_1.8.2.4
## [22] cachem_1.0.8 commonmark_1.9.1 igraph_2.0.3
## [25] lifecycle_1.0.4 iterators_1.0.14 pkgconfig_2.0.3
## [28] Matrix_1.6-5 R6_2.5.1 fastmap_1.1.1
## [31] GenomeInfoDbData_1.2.11 digest_0.6.33 Exact_3.2
## [34] aplot_0.2.2 colorspace_2.1-0 patchwork_1.2.0
## [37] S4Vectors_0.40.2 labeling_0.4.3 fansi_1.0.6
## [40] timechange_0.3.0 httr_1.4.7 abind_1.4-5
## [43] mgcv_1.9-1 compiler_4.3.3 proxy_0.4-27
## [46] bit64_4.0.5 withr_3.0.0 backports_1.4.1
## [49] carData_3.0-5 highr_0.10 ggsignif_0.6.4
## [52] MASS_7.3-60.0.1 biomformat_1.30.0 gld_2.6.6
## [55] tools_4.3.3 ape_5.8 glue_1.7.0
## [58] ggnested_0.1.0 nlme_3.1-164 rhdf5filters_1.14.1
## [61] gridtext_0.1.5 grid_4.3.3 gridBase_0.4-7
## [64] cluster_2.1.6 reshape2_1.4.4 ade4_1.7-22
## [67] generics_0.1.3 gtable_0.3.5 tzdb_0.4.0
## [70] class_7.3-22 lmom_3.0 hms_1.1.3
## [73] xml2_1.3.6 car_3.1-2 utf8_1.2.4
## [76] XVector_0.42.0 BiocGenerics_0.48.1 markdown_1.12
## [79] foreach_1.5.2 pillar_1.9.0 yulab.utils_0.1.4
## [82] vroom_1.6.5 splines_4.3.3 ggtext_0.1.2
## [85] treeio_1.24.3 survival_3.6-4 bit_4.0.5
## [88] tidyselect_1.2.1 Biostrings_2.70.3 knitr_1.46
## [91] IRanges_2.36.0 stats4_4.3.3 xfun_0.43
## [94] expm_0.999-9 Biobase_2.62.0 stringi_1.8.3
## [97] lazyeval_0.2.2 ggfun_0.1.4 yaml_2.3.7
## [100] boot_1.3-30 evaluate_0.23 codetools_0.2-20
## [103] ggplotify_0.1.2 cli_3.6.1 munsell_0.5.1
## [106] jquerylib_0.1.4 Rcpp_1.0.12 GenomeInfoDb_1.38.8
## [109] parallel_4.3.3 bitops_1.0-7 plotwidgets_0.5.1
## [112] mvtnorm_1.2-4 tidytree_0.4.6 scales_1.3.0
## [115] e1071_1.7-14 crayon_1.5.2 rlang_1.1.1
## [118] cowplot_1.1.3